Knowledge-Guided Deep Learning with Clinical EEG Biomarkers for Automated Dementia Detection and Staging.

Background

Early detection of dementia is essential for timely intervention, yet existing diagnostic approaches remain costly, invasive, or dependent on specialized expertise. Electroencephalography (EEG) offers a non-invasive and accessible alternative; however, purely data-driven deep learning models may overlook clinically established neurophysiological biomarkers, particularly in the challenging detection of mild cognitive impairment (MCI).

Methods

We propose the Clinical EEG Feature-Augmented Network (CEFA-Net), a knowledge-guided deep learning framework that systematically integrates automatic representation learning from raw multichannel EEG with clinically validated neurophysiological biomarkers. The architecture combines three complementary convolutional pathways capturing multi-scale temporal dynamics with domain-informed feature representations, enabling both data-driven discovery and clinically grounded interpretation. Task-specific optimization strategies-including focal loss, class-aware augmentation, and validation-guided ensemble weighting-were employed to enhance robustness under class imbalance. The model was evaluated on the large-scale the Chung-Ang University Hospital EEG (CAUEEG) dataset (1379 recordings from 1155 patients) across binary abnormality detection and three-class dementia staging tasks.

Results

CEFA-Net achieved 81.02% accuracy (macro F1: 81.15%) for dementia staging and 87.15% accuracy (macro F1: 87.41%) for abnormality detection, outperforming baseline methods by 6.75-9.10 percentage points (p < 0.001). Notably, the proposed framework substantially improved MCI detection (F1-score: 78%), representing a 14-point gain over traditional machine learning approaches. Ablation analyses confirmed that clinical biomarker integration and multi-model fusion provide complementary diagnostic value. In an additional patient-disjoint evaluation using the no-overlap partitions, CEFA-Net achieved 85.40% accuracy for abnormality detection and 73.80% accuracy for dementia staging, demonstrating generalization to subjects completely excluded from the training data.

Conclusions

These findings demonstrate that knowledge-guided integration of clinical biomarkers with deep representation learning can significantly enhance EEG-based dementia detection. CEFA-Net offers a clinically aligned and computationally efficient solution, supporting its potential for real-world screening and early diagnostic workflows.